Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
gapminder %>%
filter(year == 1952) %>%
ggplot(aes(gdpPercap, lifeExp, size = pop)) +
geom_point()
Looking at the plot without the log10 scale on x axis makes it clear why it is needed. The outlier forces the x-axis to be wider, making the plot unclear - it is hard to discern any patterns. We could remove the outlier:
gapminder %>%
filter(year == 1952) %>%
filter(gdpPercap < 30000) %>%
ggplot(aes(gdpPercap, lifeExp, size = pop)) +
geom_point()
> then, the logscale isn’t strictly necessary, although the data is
still better presented with log, scince it is forced into a pattern that
is similar to a linear increase in lifeexp.
gapminder %>%
filter(year == 1952) %>%
filter(gdpPercap > 30000)
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
Kuwait is the outlier.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options(scipen=10000) # This fixes the scientific notation
gapminder %>%
filter(year == 2007) %>%
ggplot(aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
xlab("GDP per capita") +
ylab("Life expectancy") +
labs(color = "Continent",
size = "Population",
title = "Life expectancy on GDP in 2007")
gapminder %>%
filter(year == 2007) %>%
arrange(desc(gdpPercap)) %>%
top_n(5)
## Selecting by gdpPercap
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
Norway, Kuwait, Singapore, US, Ireland
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)ani <- gapminder %>%
ggplot(aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
xlab("GDP per capita") +
ylab("Life expectancy") +
labs(color = "Continent",
size = "Population",
title = "Year: {frame_time}") +
transition_time(year)
ani
see above
gapminder_unfiltered dataset and
download more at https://www.gapminder.org/data/ ]I’m interested in Europe, and whether different regions might have similar life exp and GDP. Specifically I believe the nordic contries have a higher lifeexp than the rest of europe. Lets investigate!
europe <- gapminder %>% # filter to only look at europe
filter(continent == "Europe")
europe$nordic <- ifelse(europe$country == "Denmark" | # new column identifying nordic contries
europe$country == "Sweden" |
europe$country == "Norway" |
europe$country == "Finland" |
europe$country == "Iceland",
"Nordic", "Other")
ani <- europe %>%
ggplot(aes(gdpPercap, lifeExp, color = nordic)) +
geom_point() +
scale_x_log10() +
xlab("GDP per capita") +
ylab("Life expectancy") +
labs(color = "Nordic",
title = "Year: {frame_time}") +
transition_time(year)
ani
Having the Nordic contries in red and the rest of europe in blue, makes it easy to identify a trend of rich and longlived nordic contries. Several other contries are also doing well, but who they are isn’t obvious with this plot :)